Whole slide image (WSI) processing is becoming part of the key components of standard clinical diagnosis for various diseases. However, the direct application of conventional image processing algorithms to WSI faces certain obstacles because of WSIs' distinct property: the super-high resolution. The performance of most WSI-related tasks relies on the efficacy of the backbone which extracts WSI patch feature representations. Hence, we proposed BROW, a foundation model for extracting better feature representations for WSIs, which can be conveniently adapted to downstream tasks without or with slight fine-tuning. The model takes transformer architecture, pretrained using self-distillation framework. To improve model's robustness, techniques such as patch shuffling have been employed. Additionally, the model leverages the unique properties of WSIs, utilizing WSI's multi-scale pyramid to incorporate an additional global view, thereby further enhancing its performance. We used both private and public data to make up a large pretraining dataset, containing more than 11000 slides, over 180M extracted patches, encompassing WSIs related to various organs and tissues. To assess the effectiveness of \ourmodel, we run a wide range of downstream tasks, including slide-level subtyping, patch-level classification and nuclei instance segmentation. The results confirmed the efficacy, robustness and good generalization ability of the proposed model. This substantiates its potential as foundation model for WSI feature extraction and highlights promising prospects for its application in WSI processing.

翻译：全切片图像（Whole slide image, WSI）处理正成为多种疾病标准临床诊断中的关键组成部分。然而，由于WSI的超高分辨率特性，传统图像处理算法的直接应用面临诸多障碍。大多数WSI相关任务的性能依赖于提取WSI图像块特征表示的主干网络的有效性。为此，我们提出BROW——一种用于提取WSI更优特征表示的基础模型，该模型可便捷地适配下游任务，无需或仅需少量微调。模型采用Transformer架构，并利用自蒸馏框架进行预训练。为提升模型鲁棒性，我们采用了图像块洗牌等技术。此外，模型充分利用WSI的多尺度金字塔特性，引入额外全局视角，进一步增强了性能。我们整合私有与公开数据构建大规模预训练数据集，包含超过11000张切片、1.8亿余个提取图像块，涵盖多种器官与组织的WSI。为评估模型有效性，我们进行了涵盖切片级亚型分类、图像块级分类及细胞核实例分割的广泛下游任务测试。结果证实了所提模型的有效性、鲁棒性及良好泛化能力，这印证了其作为WSI特征提取基础模型的潜力，并凸显了其在WSI处理中的应用前景。